Event Description
In this exclusive session, you’ll witness firsthand how artificial intelligence is eliminating the traditional barriers between complex analytical thinking and execution. See live demonstrations of breakthrough technology that lets you query sophisticated trading analytics using natural language—no more wrestling with syntax or hunting through documentation.
What you’ll gain:
• Immediate productivity boost: Learn techniques to accelerate analytical workflows
• Competitive insights: Understand how AI-assisted analytics are creating new opportunities in market analysis
• Practical implementation: Get actionable strategies you can deploy immediately in your own quantitative work
• Future-ready skills: Position yourself at the forefront of the AI-driven transformation in quantitative finance
Live demonstrations will show real-world applications using cutting-edge tools, followed by an interactive Q&A where you can explore specific use cases relevant to your work.
Perfect for: Quantitative analysts, traders, risk managers, and technology professionals looking to leverage AI for competitive advantage.
Event Description
Financial regulatory agencies conduct periodic stress testing of systemically significant financial institutions to ensure they have the requisite capital to continue functioning as viable businesses during times of economic stress without jeopardizing the stability of the financial system. Manual design of these scenarios using historical data, exclusively or primarily, is hamstrung by the inherent limitations of historical experience, which may be inadequate to model unforeseen economic scenarios. To further compound the problem, correlations between macroeconomic variables may change and evolve in markedly different manner during those periods of economic malaise and a manual design of testing scenarios is likely to overlook those aspects of macroeconomic variable evolution. This talk will showcase the ability of generative AI methods to handle the twin challenges of economic scenario generation – generating realistically evolving scenarios that can capture the breadth of potential but unforeseen periods of stress.
Event description
Discover what a typical day entails for a Data Scientist, including daily tasks, challenges, and collaborative efforts with various teams. Dr. Souratosh Khan will discuss his career journey, offering insights into the experiences and skills that paved his way to becoming a successful Data Scientist. Learn about the essential skills required for the role, such as critical thinking and attention to detail, and how the Certificate in Quantitative Finance (CQF) can enhance these abilities.
Event Description
This study provides a detailed framework for incorporating currencies into multi asset portfolios, emphasizing the diversification benefits of active currency exposure. Our findings demonstrate that adding currencies to a traditional bonds and equities portfolio can materially improve risk adjusted returns. We present three systematic approaches to modeling foreign exchange within asset allocation frameworks, each targeting distinct exposures to the carry, value, and trend factors. Across all three models, active currency strategies enhance drawdown profiles and reduce overall portfolio volatility, while delivering incremental positive returns. Because currencies lack fixed cash flows and typically generate near zero long term passive returns, pure passive investment in FX is impractical. The major contribution of this study is therefore the development of a systematic methodology for estimating and implementing active currency benchmarks—tools that investors can reliably use within portfolio construction and tactical asset allocation processes.
Event Description
Non-Maturing Deposits (NMDs) are an important part of a bank’s balance sheet, traditionally forming a stable source of funding. These products are of relevance for liquidity/funding risk as well as interest rate risk management. The latter, in particular, has become increasingly significant given the recent three and half years of rapid transition from more than a decade of low and broadly flat interest rates, into a new and very different environment of high interest rates. Understanding the impact of drivers such as interest rates on NMD balances is of ever greater importance for effective risk management. We discuss how to design models to predict the behaviour of non-maturing deposits balances and the critical role such models can play. We also touch on key design principles to develop multi use NMD models that can support different business areas across the bank.
Event Description
Traditional asset allocation frameworks require the estimation of expected returns and covariance matrices for constructing multi-asset portfolios. In practice, these estimations pose challenges that make them unreliable for optimization. In this paper, we introduce a systematic risk-based asset allocation framework which bypasses explicit returns forecast.
For the estimation of the covariance matrix, we employ Hierarchical Group Lasso (HGL) using a factor risk model with enhanced stability imposed by the sparsity on factor loadings. To refine the construction of the tactical asset allocation, we then introduce price-based signals, including momentum and low beta for traditional investments. For alternative investments, such as private assets and hedge funds, which typically have less frequent return observations and exhibit heavy-tailed return distributions, we incorporate their specific alphas to account for their systematic and idiosyncratic risks. These enhancements dynamically adjust exposures in response to market conditions without using return forecasts.
Our methodology bridges the gap between strategic asset allocation (SAA) and tactical asset allocation (TAA) decisions, offering a scalable and adaptable solution for institutional portfolios. By prioritizing a risk-based optimization and dynamic tactical adjustments, our framework enhances robustness and flexibility in asset allocation.
Event Description
Large Language Models (LLMs) are increasingly being integrated with reinforcement learning (RL) to push the boundaries of generalist AI agents. In finance, where real-time decision-making is critical, test-time compute efficiency plays a pivotal role in ensuring models can adapt dynamically to evolving market conditions. In-context reinforcement learning (ICRL) is emerging as a transformative approach, enabling LLMs to learn and refine on the fly without explicit fine-tuning. ICRL enhances adaptability in trading, risk assessment, and portfolio optimization. This paradigm shift moves us closer to AI agents capable of robust decision-making, paving the way for more autonomous and generalizable systems in high-stakes applications.
